Baseline win rates for neural-network based trading algorithms

Andreas Krause, Michael Fairbank

Research output: Contribution to conferencePaperpeer-review

2 Citations (SciVal)
78 Downloads (Pure)


Neural networks and other machine-learning systems are used to create automatic financial forecasting and trading systems. To aid comparison of such systems, there is a need for reliable performance metrics. One such metric that may be considered is the win rate. We show how in certain circumstances the win-rate statistic can be very misleading, and to counter this, we propose and define baseline win rates for comparison. We develop empirical and closed-form models for such baselines and validate them against financial data and a neural forecaster.
Original languageEnglish
Publication statusPublished - 19 Jul 2020
EventIEEE WCCI 2020 - Glasgow
Duration: 19 Jul 2020 → …


ConferenceIEEE WCCI 2020
Period19/07/20 → …


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